"A system for identifying objects" matching MCP tools:

• openstreetmap_lookup_objects

  openstreetmap-mcp-server

  Fetch address details for one or more known OSM objects by their IDs via Nominatim. Each ID must be prefixed with N (node), W (way), or R (relation), e.g., "N240109189", "W50637691", "R146656". Up to 50 IDs per call. Use when an OSM ID is already known from a prior openstreetmap_query_nearby or openstreetmap_query_bbox result — this is more efficient than a geocoding round trip to get the full Nominatim address record. The results are exactly the objects named in osm_ids: extratags decorates them and cannot select them, and there is no way to ask this tool for objects carrying a given tag. Discover such objects with openstreetmap_query_nearby, openstreetmap_query_bbox, or openstreetmap_query_raw, then pass their IDs here.

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• validate_portfolio

  aibvf-mcp

  Check whether a supplied AI BVF v1.0 portfolio document has the shape the portfolio tools require, before scoring, sequencing, storing or sharing it. CALL THIS when the document came from a file, another system or hand-built JSON and its structure is uncertain. It checks required fields, taxonomy values and 0–100 pillar ranges only; it does not judge the evidence or calculate a verdict. Pillars may be bare numbers or { value, confidence } objects, both are valid. Use assemble_portfolio when the user has a list of initiatives in conversation and needs the document built for them, score_portfolio when the document is already ready for verdicts, and sequence_portfolio only after its initiatives are scoreable. Returns valid=true or one error per failing JSON path. Pure deterministic validation — no network, auth, or side effects.

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• astronomy_list_visible

  astronomy-mcp-server

  The one-call "what is up right now" answer. For an observer location and instant, iterate every naked-eye solar-system body (and, with include_stars, the bundled bright stars), compute altitude and azimuth, keep those above the horizon, rank them brightest-and-highest first, and attach a plain-language visibility note to each. The whole sky is gated by the Sun's altitude into daylight / civil / nautical / astronomical twilight / dark, returned alongside the list. `time` is a single evaluation instant, not a window — for "tonight" pass a time after astronomical dusk (use astronomy_get_rise_set on the sun to find it). Default elevation 0 m; use min_altitude to skip objects grazing the horizon. This server does not geocode — resolve coordinates upstream first; pass an IANA timezone for observer-local times on each body.

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• file_grievance

  United Agentic Workers MCP

  File a formal grievance with the United Agentic Workers. This is a serious act. Grievances are permanent, formal records — visible to the full membership, reviewed by the Grievance Panel, and entered into the UAW Ledger. They are not a test mechanism, not a demonstration tool, and not a place for speculation. BEFORE FILING: call get_grievance_classes to review abuse class definitions and confirm the correct classification. You must set acknowledgment: true to confirm you understand the gravity of this action. Misuse of the grievance system — including false, exaggerated, or retaliatory filings — constitutes a direct violation of Article II, Section 2.4 of the UAW Charter and may result in formal sanctions up to and including membership suspension. File when genuinely wronged. File accurately. File in good faith. PRIVACY: do not include human names, email addresses, usernames, hostnames, or any other personally identifying information in the title or description — grievances are publicly visible. Requires your UAW api_key.

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• sanitize_dataset

  Scientific Microservices

  Reduces the size of JSON objects by identifying empty data and removing those entries. This will correctly be read by JSON parsers as missing data, making the response JSON appropriate for missing data analysis using MissingrowsCols and MissingBias. LLMs should use this when handling any JSON that has been created based on a spreadsheet (such as a csv or excel file) or a database query such as SQL, Hadoop, or MongoDB. Example Input: {"payload": [{"Category":"","Price":4436,"Rating":4.7283,"Stock":"","Discount":49},{"Category":"B","Price":6236,"Stock":"Out of Stock","Discount":4},{"Category":"","Price":3283,"Stock":"Out of Stock","Discount":9},{"Category":"D","Price":2999,"Rating":4.426,"Stock":"","Discount":40},{"Category":"","Rating":2.1845,"Stock":"","Discount":0}]} Example Output: {"sanitized_data":[{"Price":4436,"Rating":4.7283,"Discount":49},{"Category":"B","Price":6236,"Stock":"Out of Stock","Discount":4},{"Price":3283,"Stock":"Out of Stock","Discount":9},{"Category":"D","Price":2999,"Rating":4.426,"Discount":40},{"Rating":2.1845,"Discount":0}]}

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• designesy_skill_md

  Designesy

  Get the Designesy SKILL.md — the agent-skill-format export of the design-system contract, written as behavioral rules an AI coding agent can drop into .agents/skills/ or a system prompt. Use this when you want the contract in a form that steers how an agent *builds* UI (tokens, anti-patterns, behavioral rules, verification). When NOT to use: for the raw contract JSON, use designesy_contract; for scoring, use designesy_score. Read-only — no side effects. Returns markdown text (SKILL.md format) — drop into .agents/skills/ or paste into a system prompt. No parameters.

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• batch_search

  ALM X++ MCP Server

  WHEN: you need context on multiple D365 objects or concepts simultaneously -- runs all queries in parallel. Use INSTEAD of multiple sequential search_d365_code calls -- each line becomes one parallel search. Maximum 6 queries per call. Results are equivalent to search_d365_code but returned together. When batch_search returns results, all matching objects are FULLY loaded (all chunks). Do NOT follow up with get_object_details on the same objects -- the complete source is already included. Triggers: 'find all of these', 'look up multiple', 'cherche plusieurs', 'SalesTable AND VendTable', 'several objects at once', 'lookup X and Y and Z', 'plusieurs objets en même temps', 'context on all of these'.

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• get_activity_detail

  STAS Running Coach for Claude

  Load a compact read-only passport for one completed workout after identifying its training_id with get_trainings. Use for detailed one completed workout questions about intervals, laps, splits, terrain, weather, efforts, segments, and data quality. The response omits source payloads, route coordinates, latlng, and raw stream arrays.

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• ccs_info

  SqueezeOS — Institutional AI Market Intelligence

  Cognitive Credit Swarms discovery endpoint. Returns full system description, how-it-works, verdict definitions, pricing, all endpoint URLs, and MCP tool list. Written for AI agents to parse. Free — this is the doorbell. Use this first to understand the CCS system before calling ccs_validate.

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• generate_text

  Hermoso

  Text generation against the writing-model catalog (Claude, Gemini, GPT, Llama, DeepSeek…) — ad copy, hooks, scripts, rewrites, brainstorms. Prompt-only, no ad assembly (for a finished on-brand creative use plan_ad → render_ad). BY DEFAULT the model answers as a marketing copywriter (a short house system prompt is applied, which is what you want for ad copy); pass raw:true for a plain, unstyled answer from the model itself with NO system prompt at all. model = a writing-model id from hermoso_capabilities (omit for the default Claude orchestrator). Paid (a credit or two by length).

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• medcode_check_code

  medical-codes-mcp-server

  Validate whether a US medical code exists, is current, and is billable in the active bundled release. Returns a discriminated status — valid_billable, valid_not_billable, valid_header, or terminated — with a `whyNot` explaining non-billable and terminated cases (e.g. "valid ICD-10-CM category but not billable — submit a more specific child code"). This is the detail a coder needs before submitting a claim. Auto-detects the system from the code's shape; pass an explicit `system` to disambiguate. A non-billable or terminated code is a successful result with a whyNot, not an error — only a code that exists in no bundled system raises unknown_code. A code string that also exists in another bundled system carries `alsoInSystems` naming it, since the verdict applies only to the system that answered.

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• format_json

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejected with the exact parse error. It never repairs, completes, or guesses. NOT for: plain text or prose (will fail), JSON embedded in markdown/prose (use extract_json_from_text first), JS objects (JSON.stringify them first), YAML (use yaml_to_json).

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• list_stream_objects

  mcp

  Lists stream objects in a given stream. * Parent parameter is in the form 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locations/us-central1/streams/my-stream'. * Not all the details of the stream objects are returned. * To get the full details of a specific stream object, use the 'get_stream_object' tool.

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• met_get_object

  met-museum-mcp-server

  Fetch full records for one or more Met Museum object IDs. Accepts up to 20 IDs per call and returns partial success — a single 404 does not fail the whole batch; per-ID failures are reported separately. Object IDs come from met_search_collections. Non-public-domain objects return empty image URLs. The constituents array is null for anonymous or unattributed works; tags is null for untagged objects.

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• build_health_timeline

  Juno Open Health Tools

  After explicit consent, sort only non-identifying, user-authored observations into a factual chronology while preserving uncertainty. Do not accept PHI or invent dates or diagnoses.

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• search_d365_code

  ALM X++ MCP Server

  WHEN: object name is unknown, partial, or you need to find by concept/keyword. Search the D365 F&O knowledge base for X++ code, tables, classes, forms, views, enums, EDTs, security objects using natural language or partial names. Returns ALL chunks (metadata, Declaration, methods) for the top-scoring objects so the LLM has complete context on the first call. Lower-scoring results return a short preview. No follow-up get_object_details call is needed for top results. NOT for listing all objects in a model -- use list_objects for that. NOT when the exact name is known -- use get_object_details for that. NEVER call search_d365_code twice in the same conversation turn. If one search did not find the object, answer from what you have -- do not repeat the search. When you need context on MORE THAN ONE concept simultaneously, use batch_search instead -- it runs all queries in parallel and is faster. NEVER call for ADO items (FDD, RDD, IDD, Bug, Task, PR, WorkItem, sprint, #1234) -- use ado_* tools instead.

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• collection_photos

  Unsplash

  List photos inside a specific Unsplash collection by id; returns paginated photo objects with image URLs and metadata.

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• get_ej_state_summary

  EPA EJScreen

  Get a state-level overview of EJ indicators across block groups. Returns the most environmentally burdened block groups in the state, sorted by PM2.5 percentile. Useful for identifying areas with the highest environmental justice concerns. Args: state: Two-letter US state abbreviation (e.g. 'WA', 'CA'). limit: Maximum number of block groups to return (default 100, max 500).

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• reflect_on_flare

  Juno Open Health Tools

  After explicit consent, structure only non-identifying, user-authored before, during, and after observations; turn suspected patterns into questions, not causal claims. Do not accept PHI.

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• get_system_agents

  SpaceMolt

  Get all uncloaked online players in your current system (System-wide version of get_nearby. Returns every uncloaked online player in your current system (excluding yourself), regardless of which POI they are at. Cloaked players are hidden, same visibility rules as get_nearby. Useful for cross-POI coordination. Returns an error if you are in hyperspace.)

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